Refactor project interface and remove unused utility files

- Update Gradio interface description to emphasize model evaluation
- Remove temporary slider and update project description
- Delete unused `goat.py` utility file and large model file
- Modify `.gitignore` to exclude additional files
- Comment out previously imported inference function

.gitignore

@@ -1 +1,3 @@
 .env
+*goat.py
+.vscode

app.py

@@ -5,7 +5,7 @@ from torchvision import transforms
 import torch
 from PIL import Image
 import numpy as np
-from utils.goat import call_inference
+# from utils.goat import call_inference / announcement soon
 import io
 import warnings
 
@@ -503,7 +503,7 @@ def predict_image_with_html(img, confidence_threshold):
 
 # Define the Gradio interface
 with gr.Blocks() as iface:
-    gr.Markdown("# AI Generated Image Classification")
+    gr.Markdown("# AI Generated Image / Deepfake Detection Models Evaluation")
     
     with gr.Row():
         with gr.Column(scale=1):
@@ -511,15 +511,8 @@ with gr.Blocks() as iface:
             confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
             inputs = [image_input, confidence_slider]
         with gr.Column(scale=2):
-            with gr.Accordion("Project OpenSight", open=True):
-                gr.Markdown("## Open source SOTA image moderation and detection tool.")
-                temp_slider = gr.Slider(
-                    0, 1,
-                    value=0.1,
-                    step=0.1,
-                    interactive=True,
-                    label="Slide me",
-                )
+            with gr.Accordion("Project OpenSight - Model Evaluations & Playground", open=True):
+                gr.Markdown("## OpenSight is a SOTA gen. image detection model, in pre-release prep.\n\nThis HF Space is a temporary home for us and the public to evaluate the shortcomings of current open source models.")
             image_output = gr.Image(label="Processed Image", visible=False)
             # Custom HTML component to display results in 5 columns
             results_html = gr.HTML(label="Model Predictions")

large_model_3lakh_v1.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c9fbb9bbfb644a42f077e20a2b4da3d7240879303474236e77d045b89fd46fff
-size 180867592

utils/goat.py

@@ -1,15 +0,0 @@
-import os
-import requests
-import json
-
-def call_inference(img):
-    files = {
-        'image': ('image.jpg', img, 'image/jpeg')
-    }
-    print(img)
-    
-    response = requests.post(
-        "https://aiwb-sightmodel.web.val.run", 
-        files=files
-    )
-    return response.json()